ESSDEarth System Science DataESSDEarth Syst. Sci. Data1866-3516Copernicus PublicationsGöttingen, Germany10.5194/essd-10-1769-2018Subglacial topography, ice thickness, and bathymetry of Kongsfjorden,
northwestern SvalbardTopography of KongsfjordenLindbäckKatrinkatrin.lindback@npolar.nohttps://orcid.org/0000-0002-5941-6743KohlerJackPetterssonRickardhttps://orcid.org/0000-0002-6961-0128NuthChristopherhttps://orcid.org/0000-0002-1063-2832LangleyKirstyMesserliAlexandraVallotDorothéeMatsuokaKenichihttps://orcid.org/0000-0002-3587-3405BrandtOlaNorwegian Polar Insitute, Framsentret, Postboks 6606, Langnes, 9296 Tromsø, NorwayDepartment of Earth Sciences, Uppsala University, Villavägen 16, 752 36 Uppsala, SwedenUniversity of Oslo, Postboks 1047 Blindern, 0316 Oslo, NorwayAsiaq Greenland Survey, Postboks 1003, 3900 Nuuk, GreenlandNorwegian Coastal Administration, Kystveien 30, 4841 Arendal, NorwayKatrin Lindbäck (katrin.lindback@npolar.no)4October20181041769178119March201828March201831August201810September2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://essd.copernicus.org/articles/10/1769/2018/essd-10-1769-2018.htmlThe full text article is available as a PDF file from https://essd.copernicus.org/articles/10/1769/2018/essd-10-1769-2018.pdf
Svalbard tidewater glaciers are retreating, which will affect fjord
circulation and ecosystems when glacier fronts become land-terminating.
Knowledge of the subglacial topography and bathymetry under retreating
glaciers is important to modelling future scenarios of fjord circulation and
glacier dynamics. We present high-resolution (150 m gridded) digital
elevation models of subglacial topography, ice thickness, and ice surface
elevation of five tidewater glaciers in Kongsfjorden (1100 km2),
northwestern Spitsbergen, based on ∼1700 km airborne and ground-based
ice-penetrating radar profiles. The digital elevation models (DEMs) cover the tidewater glaciers
Blomstrandbreen, Conwaybreen, Kongsbreen, Kronebreen, and Kongsvegen and are
merged with bathymetric and land DEMs for the non-glaciated areas. The
large-scale subglacial topography of the study area is characterized by a
series of troughs and highs. The minimum subglacial elevation is -180 m
above sea level (a.s.l.), the maximum subglacial elevation is
1400 m a.s.l., and the maximum ice thickness is 740 m. Three of the
glaciers, Kongsbreen, Kronebreen, and Kongsvegen, have the potential to
retreat by ∼10 km before they become land-terminating. The compiled
data set covers one of the most studied regions in Svalbard and is valuable
for future studies of glacier dynamics, geology, hydrology, and fjord
circulation. The data set is freely available at the Norwegian Polar Data Centre
(https://doi.org/10.21334/npolar.2017.702ca4a7).
Introduction
Ocean waters around Svalbard are warming, which in combination
with the overall atmospheric warming has made Svalbard's tidewater glaciers
particularly vulnerable to climate change (Nuth et al., 2013). Air
temperatures have increased steadily over the last 4 decades, similar to
the rest of the Arctic (Overland et al., 2004). Summer temperatures have the
strongest influence on Svalbard glacier mass balance (van Pelt et al., 2012),
and the recent summer warming has led to increasing rates of mass loss
(Kohler et al., 2007). The current overall mass balance for Svalbard glaciers
is negative (Moholdt et al., 2010; Nuth et al., 2010; Wouters et al., 2008),
with tidewater glaciers having the greatest retreat rates overall (Nuth et
al., 2013). More than half of Svalbard's total land area of ∼60000 km is covered by glaciers (König et al., 2014). Over 1100
glaciers are larger than 1 km2, and of these, 163 (15 %) are
tidewater glaciers. In terms of area, more than 60 % of all glacier
fronts terminate at sea, and the total length of calving ice-cliffs around
Svalbard is estimated to be ∼860 km (Błaszczyk et al., 2009). A
significant portion of the meltwater is delivered to the ocean at calving
glacier fronts. With further warming in the Arctic, we expect the Svalbard glaciers to continue to retreat, and concomitant declines in the number of
tidewater calving glaciers and total length of calving fronts around
Svalbard, providing a contribution to rising global sea levels.
Radar survey lines collected between 2004 and 2016.
Kongsfjorden, tidewater glaciers, icefields, the peninsula Brøggerhalvøya, and the research
town Ny-Ålesund are marked in the map. The small inset map shows the location of Kongsfjorden in
Svalbard. The blue areas are sea, green areas are land, and white areas are
glacierized regions (in 2009). The black lines indicate the location of the
profiles in Fig. 4 and the boxes show the coverage of Figs. 5
and 6. Grid projection is
Universal Transverse Mercator Zone 33W.
Glacier front areas are important feeding areas for seabirds and marine
mammals (Kovacs et al., 2011; Kovacs and Michel, 2011; Loeng et al., 2005; Lydersen et al., 2014). In summer, glacier
meltwater flows on, in, and under the glacier towards the front. This
meltwater is typically discharged below the seawater surface, often at the
base of the calving front. The relatively low density of these fresh waters
forces them to rise rapidly, entraining large volumes of ambient fjord water.
These meltwater plumes can breach the surface, then flow outward towards the
mouth of the fjord, further entraining subsurface water. In Svalbard, several
bird species can be found in large numbers, up to thousands of individuals,
at tidewater glacier fronts. The birds are often found in the so-called
“brown zone”, the meltwater plume, which is ice-free and muddy due to
upwelling suspended sediments and currents. These brown zones are also
foraging hotspots for Svalbard's ringed seals and white whales (Lydersen et
al., 2014). When the tidewater glaciers retreat so much that they become
land-terminating, outflow into the fjord will only occur via surface
drainage, just as with any un-glaciated fjord, with a cap of fresh river
water flowing over the denser ocean water. This will lead to fewer nutrients
and plankton being brought to the surface from the fjord bottom, which is
likely to affect fjord ecosystems. Changes in freshwater flux from western
Svalbard glaciers may also, in extreme climate warming scenarios, disturb
deep-water production on the Svalbard shelf (Hagen et al., 2003). The
amplified climatic warming at northern high latitudes (Serreze and Barry,
2011) makes Svalbard glaciers prime targets for understanding not only
glacial dynamics but also the effects of ongoing climate change on glaciers,
oceans, and ecosystems. To model future scenarios of fjord circulation and
glacier dynamics, knowledge on the subglacial topography and bathymetry under
the retreating glaciers is vital. Here, we present high-resolution (150 m
gridded) digital elevation models (DEMs) of the subglacial topography, ice
thickness, and elevation of five tidewater glaciers in Kongsfjorden,
northwestern Spitsbergen, near Ny-Ålesund (78.9∘ N,
12.4∘ E).
Study area
Kongsfjorden is the southern branch of the Kongsfjorden–Krossfjorden system
that merges towards the open sea, in a large submarine trough,
Kongsfjordrenna, which channelled a fast-flowing ice stream during the last
glacial maximum (Ingólfsson and Landvik, 2013; Ottesen et al., 2005).
Kongsfjorden is ∼20 km long and between 4 and 10 km wide and covers an
area of ∼200 km2 and a water volume of ∼30 km3 (Ito
and Kudoh, 1997). The maximum depth in the outer part of the fjord is 350 m and
100 m in the inner fjord. The mouth of the fjord lacks a well-defined sill
and is therefore interconnected with neighbouring water masses on the West
Spitsbergen Shelf, including Atlantic Water (Svendsen et al., 2002). Five
tidewater glaciers terminate in Kongsfjorden (Fig. 1): Blomstrandbreen,
Conwaybreen, Kongsbreen (with a north and south branch around Ossian
Sarsfjellet), Kronebreen, and Kongsvegen. Kronebreen is among the
fastest-flowing glaciers in Svalbard, with speeds up to ∼3 m d-1
(Schellenberger et al., 2015). Upglacier from Kongsbreen and Kronebreen are
two large icefields, Holtedahlfonna (named Dovrebreen in the upper part) and
Isachsenfonna. In the following section, a short summary of the
glacio-geomorphological setting of the study area is presented.
Glacio-geomorphological setting
The youngest deposits in Kongsfjorden are of Quaternary age and these
landforms around Kongsfjorden are shaped by glacial activity (Ingólfsson
and Landvik, 2013). Brøggerhalvøya and the areas to the north were
probably completely ice-covered during the last Weichselian ice age. Compared
to most other places in Svalbard, the Kongsfjorden area shows a more complete
glacial sedimentary record dating back to before the last interglacial,
the Eemian (Landvik et al., 2005). Together with Bellsund and Isfjorden,
Kongsfjorden was one of the largest outlets for palaeo-ice streams in western
Svalbard. The glaciers along the west coast of Svalbard had a complicated
topographically controlled configuration during the Weichselian (Howe et
al., 2003). The ice stream in Kongsfjordrenna was fed by ice draining through
the deep fjord systems of Kongsfjorden and Krossfjorden, which drained a
large section of the ice fields over northwestern Spitsbergen. Adjacent to the ice
stream there were sharp boundaries to dynamically less active ice.
The glaciers started to retreat during the early Holocene and the region was
likely largely ice-free until the neoglacial advance ∼4.5 thousand
years ago. The bedrock has a relict subglacial, ice-scoured topography from
the glacial re-advances of the Weichselian glaciation, with drumlins and
glacial flutes common across the sea floor (Howe et al., 2003).
Brøggerhalvøya shows four isostatically induced cycles of emergence out
of the sea during the Weichselian glaciation (Miller et al., 1989), with
beach ridges up to the marine limit at ∼80 m above sea level (a.s.l.).
The current isostatic uplift rate is 8 mm y-1 (Kierulf et al., 2009).
The bottom of inner Kongsfjorden has waveform morphology interpreted as
moraines, partly originating from surges (Howe et al., 2003). Three glaciers
are documented as surge type glaciers: Kronebreen–Kongsvegen surged around
1869 and 1897 (Bennett et al., 1999), Kongsvegen around 1948 (Liestøl,
1988; Woodward et al., 2002), and Blomstrandbreen, possibly between 1911 and
1928 (Burton et al., 2016), around 1960 (Hagen et al., 1993), and recently in
2010 (Mansell et al., 2012). Glacier surges lead to short-term reworking of
sediments and deposition of sediment lobes containing massive glaciomarine muds with sedimentation accumulation rates up to 30 cm y-1. Suspension
settling from meltwater plumes and ice rafting are the dominant sedimentary
processes, leading to the deposition of stratified glaciomarine muds with
clasts from melting icebergs. The fjord topography has been smoothed by
bottom currents.
Data and methods
We map the glacier beds with ice-penetrating radar (Dowdeswell and Evans,
2004). Our analysis builds on extensive radar campaigns conducted in the area
from 2004 to 2016 (Fig. 1). Earlier campaigns (1988 to 2005) covered the
upper parts of the glaciers, but the airborne radar failed to detect the bed
in the lower reaches. This was caused by a too-high radar frequency (dictated
by limitations on antenna size on an airplane) and too-high travel speed with
respect to the data acquisition rate, as well as radar clutter from the rough
surface, crevasses, and water within the glacier (Hagen and Sætrang,
1991). In recent years, radar surveys have successfully detected the bed in
the lower parts of the glaciers using a lower frequency set-up mounted on a
helicopter frame (Fig. 2). In the following sections, we describe the
methods used to collect, process, and interpolate the radar data sets into the
final products of gridded subglacial elevation and ice thickness. Surveys of
crevassed glaciers from helicopters are less common (e.g. Blindow et
al., 2012; Kennett et al., 1993; Langhammer et al., 2017; Rutishauser et
al., 2016) than surveys from fixed-wing airplanes (e.g. Bamber et al., 2013;
Fretwell et al., 2013; Morlighem et al., 2017). Therefore, we describe the
set-up in detail.
(a) Helicopter with radar frame. Photo: Nick Hulton.
(b) Helicopter wooden frame (1) from above, with transmitter (Tx),
receiver (Rx), two batteries and four plastic pipes for holding the antennas, and (2) from the side,
with two fins on one side that
function as wind rudders to prevent the frame from spinning. The antennas are
connected to the Tx and Rx and fixed to the frame extending out on the
plastic rods.
Examples of processed radar images collected on
(a) Kongsvegen by snowmobile and (b) Holtedahlfonna by
helicopter. Locations of the profiles are marked in Fig. 1 with black lines.
Radar systems and uncertaintiesRadar data collected 2014 to 2016
During early spring (April to May) in 2014, 2015, and 2016 we collected ∼1300 km of common-offset radar profiles with an impulse radar system that
was either suspended under a helicopter (for crevassed areas; Fig. 2a) or
towed behind a snowmobile. The system is based on radar developed for
surveying ice thickness on the Greenland ice sheet (Lindbäck et
al., 2014). The radar system consisted of resistively loaded half-wavelength
dipole antennas of 10 MHz centre frequency. We used a commercial
off-the-shelf Kentech impulse transmitter with an average output power of
35 W and a pulse repetition frequency of 1 kHz. The trace acquisition was
triggered by the direct wave pulse between transmitter and receiver. The
14-bit A/D converter sampled two channels at 125 MHz sampling frequency,
with different sensitivity ranges. One channel was attenuated with 20 dB to
record both the surface and the bed return.
Using the helicopter-based system, we surveyed the glaciers at a nominal speed
of ∼40 km h-1, along tracks separated by ∼0.5 to 1 km. By
stacking 125 traces, a mean trace spacing of 4 m was achieved. We mounted
the system on a 3×3 m wooden frame, with extended wooden arms and
plastic rods for the antenna (Fig. 2b). The frame was suspended 20 m below
the helicopter. The radar was controlled by wireless connection to a PC
inside the helicopter. We used the ground-based system to survey the
snowmobile-accessible Kongsvegen glacier. The system was mounted on two sleds
and towed behind the snowmobile at a speed of ∼20 km h-1.
Stacking 125 traces resulted in a mean trace spacing of 2 m. We positioned
the traces by using data from a code-phase global positioning system (GPS)
receiver in 2014 and 2015 and a carrier-phase dual-frequency GPS receiver in
2016, mounted on the radar receiver box 1.5 m in front of the common
mid-point along the travelled trajectory on the helicopter frame and 15 m
from the common mid-point on the snowmobile. For the dual-frequency receiver
we processed the data kinematically using the Canadian Spatial Reference
System precise point positioning service (Natural Resources Canada, 2017).
We applied several corrections and filters to the radar data:
(1) a Butterworth bandpass filter, with cut-off frequencies of 2 and 50 MHz,
to remove unwanted frequency components in the data; (2) normal move-out
correction to correct for antenna separation (including adjusted travel times
for the trigger delay); (3) rubber-band correction to re-sample the data to a
uniform trace spacing; and (4) two-dimensional frequency wave-number
migration (Stolt, 1978) to collapse hyperbolic reflectors back to their
original positions in the profile direction. On the high gain channel, we
applied a spreading and exponential compensation (SEC) gain to amplify bed
returns. The surface and bed returns were digitized semi-automatically with a
cross-correlation picker (Irving et al., 2007) at the first break of the bed
reflection. We calculated ice thickness from the picked travel times of the
bed return using a constant radio-wave velocity of 169 mµs-1
for ice. For the airborne profiles, we removed the travel times to the
surface return using a constant radio-wave velocity of
300 µms-1 for air. We converted the GRS80 ellipsoidal
heights to heights above sea level with a geoid model developed by the
Norwegian Polar Institute, where the average geoid height is ∼35±0.5 m in the study area relative to the ellipsoid. Figure 3 shows examples
of processed radar images.
Radar data collected 2004 to 2010
In addition to the data collected in this study, we used two additional data
sets of unpublished radar data collected earlier by the Norwegian Polar
Institute on (1) Dovrebreen in 2004 and 2005, and (2) Kronebreen and
Holtedahlfonna in 2009 and 2010. We did not use additional data sets
collected in Kongsvegen in 1988 (ice thickness; Hagen and Sætrang, 1991)
and 1995 (subglacial elevation and ice thickness; Melvold and Hagen, 1998),
because these data sets had large (>50 m) differences in subglacial
elevation. This is because the data were collected over 20 years ago and
possibly significant changes in glacier surface and subglacial sediment may
hinder accurate estimates of subglacial elevations from these old
ice-thickness data. Here follows a short summary of the two included data
sets:
The Dovrebreen campaign. The data set consists of ∼22 km of
radar profiles collected on the upper parts of Dovrebreen, during an ice
coring campaign (Beaudon et al., 2013; Sjögren et al., 2007). The
subglacial elevation and ice thickness were measured in April 2004 and 2005,
with a 10 MHz centre frequency impulse radar. A single channel impulse radar
based on a Narod transmitter (Narod and Clarke, 1994) and a 12-bit A/D
converter in the receiver were used with restively loaded dipoles as
antennas. The radar was operated at both 100 MHz sampling frequency and at
200, 300, and 500 MHz sampling frequency using repetitive sampling. The
repetitive sampling gave a non-uniform sampling frequency in the scan, and
the data had therefore been resampled to an equal time base between the samples with linear interpolation.
An antenna separation of 20 m was used. The antennas were configured with
the transmitter in the back and the receiver approximately 25 m behind a
snowmobile. The profiles were positioned with a code-phase GPS receiver
attached to the radar receiver and the position was recorded each second.
The Kronebreen and Holtedahlfonna campaign. The data consist of
∼340 km of radar profiles collected in 2009 to 2010, where both
helicopter and snowmobiles were used. Data were collected with an impulse
dipole radar comprising a Kentech pulser (average output power of 35 W),
10 MHz resistively loaded wire dipole antennas, and a 12-bit A/D converter.
The helicopter and ground-based system was similar to the one previously
described (see Sect. 3.1.1). The A/D converter sampled with two channels at
50 MHz sampling frequency. Five traces were stacked in flight, and further
stacking was done during post-processing. Positioning was made with a
code-phase GPS receiver attached to the radar receiver.
Errors in the subglacial elevation for each data point:
(a) radar error consisting of the technical and theoretical capacity
of the radar systems, (b) positioning error, and (c) the
total error when combining radar and positioning errors. Grid projection is
Universal Transverse Mercator Zone 33W.
Radar system errors and uncertainty
We used standard analytical error propagation methods (Lapazaran et
al., 2016; Taylor, 1996) to calculate the error in subglacial elevation for
each data point:
εbeddata=εradar2+εxy2,
where εradar was the error in the radar acquisition and
εxywas the positioning error. The error in radar acquisition
was calculated by the following:
εradar=12v2⋅εt2+t2⋅εv2,
where v was the radio-wave velocity used for time-to-depth conversation,
t was the two-way-travel time of the radio wave and εt and
εv were the errors in t and v respectively. We used a
constant wave-propagation speed for the ground-based and airborne surveys
(169 mµs-1). Wave velocity can vary spatially, depending
mostly on density. Profiles were collected in the ablation and accumulation
zone with a snow and firn cover of up to 20 m thick in the upper parts of
Dovrebreen (Beaudon et al., 2013; Woodward et al., 2003). We used a typical
variation of 4 % of glacier ice density for the calculation of
εv (Seligman, 1936). Variations in the wave velocity can also
occur because of varying ice temperature and the presence of inhomogeneities
and liquid water in the ice (Drewry, 1975). These effects are expected to
have a small impact on the average velocity for the whole ice column, while
water content in the ice can influence the velocity in a substantial way. In
most parts of the study area the ice is cold (Beaudon et al., 2013; Woodward
et al., 2003) and there are limited amounts of liquid water. We therefore
neglect variations of velocity due to water content. The upper parts of
Holtedahlfonna contain a firn aquifer (Christianson et al., 2015), but it
comprises a small part of the total glacierized area, and is not accounted
for. For εt we calculated the range resolution, which is the
accuracy of the measurement of distance between the antenna and the bed and
can be determined from the characteristics of the source pulse (i.e.
bandwidth) and the digitization frequency. The range resolution for the data
collected in this study was estimated at 8.5 m. We also included the
vertical resolution, by taking the inverse of the radar frequency (Lapazaran
et al., 2016). This results in values of εradar between
8.5 m (thin ice) and 30.5 m (thick ice) with a mean value of 14.3 m and
standard deviation of 4.2 m (Fig. 4a). We calculated the positioning error
εxy at each data point depending on the bed slope angle along
the profile, following the method by Lapazaran et al. (2016). We assumed a
helicopter-travel speed of 40 km h-1, snowmobile-travel speed of
20 km h-1, and TGPS≤TGPR (case a′ in
Appendix B of Lapazaran et al. 2016). This produced values of
εxy between 0 (flat bed) and 76.0 m (steep bed ∼90∘), with a mean value of 1.4 m and standard deviation of 2.1 m
(Fig. 4b). The total error in subglacial elevation along the profiles (Eq. 2)
varied between 8.5 and 78.0 m, with a mean of 14.5 m and standard deviation
of 4.3 m (Fig. 4c).
To test the consistency between the data sets we also compared the crossover
differences in the subglacial elevation estimates between different profiles
and data sets. The data set collected in this study (2014 to 2016) had a
median crossover misfit in subglacial elevation of 11.1 m with a standard
deviation (σ) of 17.4 m based on 208 crossing points. The Dovrebreen
campaign data set (2004 to 2005) had a median crossover misfit of 13.1 m
(σ=12.7 m) based on 85 crossing points. The Kronebreen and
Holtedahlfonna campaign data set (2009 to 2010) had a median crossover misfit
of 3.9 m (σ=13.0 m) based on 136 crossing points. As the crossover
analysis within the same data set does not capture systematic errors between
the different data sets, we also did a comparison between the data sets. When
we ran a crossover analysis between all the data sets the median misfit was
9.2 m (σ=15.7 m).
Surface and bathymetric elevation data
To obtain surface elevation for the study area that is most temporally
consistent with the acquired helicopter thickness measurements, we used
TanDEM-X monostatic radar images (Moreira et al., 2004) acquired on
20 December 2014. These images were processed by differential interferometry
using Gamma Software (e.g. Neckel et al., 2013; Rankl and Braun, 2016) as
precise orbital information is not publicly available. The monoscopic images
were first co-registered to each other and to a previous baseline DEM derived
from the TanDEM-X intermediate DEM (Wessel, 2016). After phase filtering, the
differential phase was unwrapped using a minimum cost flow (MCF) algorithm
and triangulation to provide elevation differences in metres between the
intermediate DEM and that from the monoscopic images. Unwrapping was
successful over the relatively flat terrain, with no apparent blunders even
over steeper terrain. These differences were then added back to the TanDEM-X
intermediate DEM to provide elevations from 20 December 2014 at a 12 m
resolution. The obtained 2014 TanDEM-X elevations were for the underlying ice
and ground surface as the X-band satellite radar wave can penetrate through
the winter snowpack, providing a reflector from the ground and ice interface.
On stable terrain surrounding the glaciers, the 2014 TanDEM-X DEM shows
little bias, with a standard deviation of ∼4 m compared with the 2009
aerial-photogrammetric DEM from the Norwegian Polar Insitute (2014), which
has a 5 m gridded resolution and a stated accuracy of ∼2 to 5 m.
Elevation changes between 2009 and 2014 have mostly occurred close to the
margins of the tidewater glaciers (<5 km), with up to 5 m of surface
lowering (Cesar Deschemps, personal communication, 2017). This TanDEM-X DEM
was merged with the NPI (2014) DEM to cover Blomstrandbreen, which is located
outside of the TanDEM-X DEM, and downsampled to 150 m.
The offshore bathymetric DEM was compiled by the Norwegian Mapping Authority
Hydrographic Service and is a publicly available data product (Kartverket,
2018). Data were acquired in 2000 with an EM 1002 multi-beam echo sounder. In
2007, 2010, and 2011 an EM 3002 multi-beam echo sounder was used. They derived
the 50 m grid DEM in 2014 with the software QPS Fledermaus and CARIS
HIPS/CARIS BathyDataBASE. The surface and bathymetric DEMs were point sampled
and added to the subgrid of the radar data, described in detail below.
(a) Subglacial, bathymetric, and land elevation with 100 m
elevation contours (grey lines). (b) Ice thickness in 2014 with
100 m elevation contours (grey lines) and glacier surface elevation
catchments (black polygons). Hillshade image in the background from Fig. 5a.
Statistics for each glacier are specified in Table 1. (c) Surface
elevation in 100 m contours showing the extent of the TANDEM-X (TDX) DEM
(blue polygon). Background image is Sentinel-2 satellite image taken on
10 July 2016 (Copernicus, 2016). Grey areas in all figures are glaciers not
covered in the study. Grid projection is Universal Transverse Mercator Zone
33W.
Assimilation of the data sets
We combined the different data sets of subglacial, land, and bathymetry
elevation to a final gridded elevation DEM. The measuring interval for the
radar data sets are dense along the profiles, with a data point spacing of
∼5 m, compared with ∼50 to 1000 m spacing between individual
profiles. As this non-uniform spacing is not optimal for gridding algorithms,
we sub-gridded data sets into a 100 m pseudo-grid to reduce the data density
along individual profiles. The subgrid was produced by calculating the median
values for the points that fell within the distance of half the grid cell. To
prevent steps at the borders between the subglacial and proglacial DEMs we
point-sampled the land-topography and bathymetric DEMs outside the
glacierized areas and added these points to the subgrid. We used a universal
kriging algorithm (e.g. Isaaks and Srivastava, 1989) for the interpolation.
To calculate glacier ice thickness in 2014, we subtracted the subglacial DEM
from the combined TanDEM-X DEM and NPI DEM. We did this instead of using ice
thickness measurement for each radio echo-sounding data set, to make the
subglacial DEM more consistent over the area, as the ice thickness has
changed since the first data were collected in 2004.
The subglacial DEM agrees well with a borehole study in Kronebreen in 2014
(How et al., 2017), with a measured subglacial elevation of -93 m a.s.l.,
where the gridded DEM predicts a depth of 90 m. To assess the error in
interpolation we cross-validated the gridded data, which is a common
validation technique to see how well an interpolated model is influenced by
the observed data. By removing one observation from the data set, the
remaining data were used to interpolate a value for the removed observation.
This process was continued for 1000 random observations in the data, where
the error is the residual between the observed and the interpolated value
(Isaaks and Srivastava, 1989). The standard deviation of the residuals was
estimated to be 18 m, and increases with distance from the profiles. To
summarize, the total accuracy of the gridded subglacial elevation depends on:
(1) the technical and theoretical capability of the radar systems,
(2) positioning errors, and (3) interpolation errors. By assessing all these
potential sources of error, we estimate the maximum vertical
root-mean-squared uncertainty in the final subglacial and ice thickness DEMs
to be approximately ±24 m.
Results
We present DEMs of subglacial topography (Fig. 5a), ice thickness (Fig. 5b),
and ice surface elevation (Fig. 5c) of a 1100 km2 area of Svalbard on a
150 m grid. The DEMs cover the tidewater glaciers Blomstrandbreen,
Conwaybreen, Kongsbreen, Kronebreen, and Kongsvegen and are merged with
bathymetric and land DEMs for the non-glaciated areas. The large-scale
subglacial topography of the study area is characterized by a series of
troughs and highs. The minimum subglacial elevation is -180 m a.s.l., the
maximum subglacial elevation is 1400 m a.s.l., and the maximum ice
thickness is 740 m. We estimate the maximum vertical root-mean-squared
uncertainty in the subglacial and ice thickness DEMs to be approximately ±24 m. The statistics for each glacier are summarized in Table 1. For
Kronebreen, which has a dense data coverage (Fig. 1), we also present a 50 m
gridded subglacial DEM (Fig. 6). In the following paragraphs, we describe the
main troughs and highs in the subglacial topography. Overdeepenings in
Blomstrandbreen and Conwaybreen (-110 and -60 m a.s.l., respectively;
Figs. 5a, 7a, and b) lie behind sills at the glacier front, which limit the
extension of the fjord further upglacier; both overdeepenings will therefore
likely be either filled with sediments or freshwater, as the glaciers
retreat. Further south, Kongsbreen consists of two tributary outlets around
the Ossian Sarsfjellet. Kongsbreen North has a deep trough beneath it with a
minimum elevation of -180 m a.s.l (Figs. 5a and 7c). The continuation of
the fjord (i.e. elevation beneath current sea level) extends 11 km inland
from the current front to north of the nunatak Steindolpen, upglacier from
Collethøgda. The fjord may possibly connect with Kronebreen in a 500 m
wide embayment, with only 10 m deep waters. Kongsbreen South has a sill at
its front, with a minimum elevation of 8 m a.s.l., preventing the embayment
in front of the glacier connecting to Kongsbreen North.
Statistics for each glacier with subglacial elevation and ice
thickness.
Subglacial, bathymetric, and land elevation of Kronebreen at 50 m
gridded resolution with 20 m elevation contours. Location of the borehole
study is marked with a black star (How et al., 2017). Grid projection is
Universal Transverse Mercator Zone 33W.
Kronebreen, the fastest flowing glacier in the fjord (Schellenberger et
al., 2015), has a trough beneath it with a minimum elevation of
-130 m a.s.l (Figs. 5a, 6 and 7d). The trough continues 10 km inland,
where it ends at a 350 m wide and 2 km long embayment with 20 m shallow
waters. Upglacier from the embayment there is a steep sill, with a minimum
elevation of 130 m a.s.l., which can also be seen at the glacier surface
(Fig. 7d), where there is a steep and heavily crevassed ice fall. Further
inland, the ice thickens again and there is a small overdeepening with a
minimum subglacial elevation of -30 m a.s.l. The high-resolution
subglacial DEM of Kronebreen has so far been used in studies of basal
sliding, subglacial hydrology, and calving (How et al., 2017; Vallot et
al., 2017, 2018).
The subglacial topography beneath Isachsenfonna consists of a 3 km wide flat
valley with a minimum subglacial elevation of 40 m a.s.l (Figs. 5a and 7c).
Holtedahlfonna has a 2 km wide valley with higher subglacial elevations,
with a minimum elevation of 120 m a.s.l, and gradually higher elevations up
on Dovrebreen (Figs. 5a and 7d). Finally, Kongsvegen, flowing in from
the southeast towards Kronebreen, has a trough beneath it with a minimum
subglacial elevation of -70 m a.s.l., which continues 9 km inland until
just north of the nunatak Vorehaugen (Figs. 5a and 7e).
Glacier surface elevation in 2014 (blue line) and subglacial
elevation (brown line) along the glacier ice flow centre lines (Fig. 5b) of
(a) Blomstrandbreen, (b) Conwaybreen,
(c) Kongsbreen and Isachsenfonna, (d) Kronebreen and
Holtedahlfonna, and (e) Kongsvegen. Dashed black line is present-day
sea level. Distance is measured from the present-day glacier front. Notice
that the x axis scale varies between the plots.
The compiled data sets of ground-based and airborne radar
surveys are freely available at the Norwegian Polar Data Centre
(10.21334/npolar.2017.702ca4a7, Lindbäck et al., 2018). The data set will be updated when the
quality of the data is improved or if new data sets become available.
Summary
Tidewater glaciers have a major influence on
circulation in the water bodies in which they sit, particularly in
constricted bays or fjords. In this study, we produced subglacial topography
and ice thickness DEMs of five tidewater glaciers in Kongsfjorden on a 150 m
grid. We also produced a 50 m gridded resolution DEM for Kronebreen, where
the data coverage is dense. The subglacial elevation and ice thickness data
consist of ∼1700 km common-offset radar profiles collected in 2004 to
2016 with an impulse radar system that was either suspended under a
helicopter (for crevassed areas) or towed behind a snowmobile. We combined the
data sets of subglacial elevation with land elevation and bathymetry
elevation DEMs to a final gridded DEM. The large-scale subglacial topography
of the study area is characterized by a series of troughs and highs, where
the glaciers Kongsbreen, Kronebreen, and Kongsvegen have the potential to
retreat by ∼10 km before they become land-terminating. The compiled
data set covers one of the most studied regions in Svalbard and is valuable
for future studies of glacier dynamics, geology, hydrology, and fjord
circulation.
KaL was primarily responsible for collecting, processing,
and analysing the data and prepared the paper with contributions from
all co-authors. JK was the project leader and was the main responsible for
fieldwork and data management. RP was primarily responsible for the radar
system used in 2014 to 2016. KaL, JK, AM, and DV collected the radar data in
the field (campaigns 2014 to 2016). CN provided the surface TanDEM-X data set.
KiL, KM, and OB provided radar data from the earlier campaigns (2004 to 2010).
The authors declare that they have no conflict of
interest.
Acknowledgements
This work was part of the TIGRIF (Tidewater Glacier Retreat Impact on Fjord
circulation and ecosystems) project, funded by the Research Council of
Norway, Oceans and Coastal Areas Programme (project 243808). Funding has also
been provided by the GLAERE project (the Polish-Norwegian Research Programme)
and TW-ICE projects (Centre for Ice, Climate, and Ecosystems of the Norwegian
Polar Institute). Field support was given from the Swedish Society for
Anthropology and Geography (SSAG), Svalbard Science Forum (SSF; RIS no. 6660)
and the Nordic Centre of Excellence SVALI. We would also like to thank Geir
Gunleiksrud and Boele Kuipers for providing the bathymetric DEM. Christopher
Nuth acknowledges funding from European Union, through the ERC (grant
no. 320816) and ESA (project Glaciers_CCI, 4000109873/14/I-NB). The
TanDEM-X DEM and IDEM were provided by the German Space Agency (DLR)
satellites TerraSAR-X and TanDEM-X (proposals XTI_GLAC6716 and
IDEM_GLAC0435). We thank Neil Ross, an anonymous referee, and editor
Reinhard Drews for reviewing the paper. We also thank Edward King and Bryn
Hubbard for reviewing an earlier version of the paper.
Edited by: Reinhard Drews
Reviewed by: Neil Ross and one anonymous referee
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